Tuesday, June 9, 2026

Orbital AI Compute Seems to be Coming, but Not at Scale, Right Away

With SpaceX going public on June 12, 2026, lots of investors will be pondering the feasibility of creating orbital data centers at scale.


But space-based data centers are not an immediate replacement for terrestrial data center alternatives for reasons of initial cost and capacity. Launch costs remain substantial.  


Potential upsides center on lower ongoing costs offsetting high upfront costs, eventually, though initial total operating costs will probably not match terrestrial alternatives:

  • Cheap/abundant power: Solar in orbit provides ~36% higher irradiance, near-constant supply (no night/clouds/weather), and very low marginal costs (projections ~$0.005/kWh vs. $0.04-0.08/kWh terrestrial wholesale). No grid connection, fuel, or large storage needed in ideal orbits.

  • Lower OpEx: Projections include 97% lower operating costs in some models (energy + cooling). No land, permitting, property taxes, or water for cooling. Avoids terrestrial delays/queues for power infrastructure.

  • Scalability and utilization: Unlimited "land" in orbit for expansion. High utilization from constant power. Falling launch costs could lead to cost parity or better for power-dominant workloads by late 2020s to 2030s.


Orbital systems could ease some important terrestrial obstacles:

  • Energy and emissions: Relies on clean solar (potentially 10x lower CO₂ emissions). Reduces strain on terrestrial grids, which often use fossil backups for data centers.

  • Resource Savings: No water consumption for evaporative cooling (a major terrestrial issue). Frees land for other uses; avoids local ecosystem/power price impacts from hyperscale farms.

  • Overall footprint: Could lower terrestrial data center growth, helping with power queues, water scarcity, and NIMBY opposition.


Of course, environmental impact is still there. Launch emissions, space debris (cluttering orbits, potential Kessler syndrome risk), manufacturing impacts and end-of-life disposal remain issues. 


Some use cases might make more sense. Workloads tolerant of moderate latency (~100-500 ms round-trip) and benefiting from proximity to space data or constant power suggest suitability for:

  • AI Inference: Querying trained models (chat, search, voice agents, video generation, back-office automation)

  • Some telemetry use cases: Onboard near-source analysis of Earth observation, climate monitoring, disaster detection (wildfires/floods), maritime surveillance, sensor apps

  • Some edge compute cases: Real-time processing for satellites, space cybersecurity or autonomous operations or resilience against terrestrial outages/disasters

  • High-Security/ Sovereign Compute: Isolated environments for sensitive data, national security, or regions with poor terrestrial infrastructure.

Public Cloud, Private Cloud or On-Prem for AI Processing?

Among the many other changes artificial computing is raising for enterprise technologists and managers, AI also creates a new framework for thinking about older issues such as "cloud or on-prem?"


The new question is: "Which workloads justify dedicated GPU ownership, and which should be rented?"


Historically, the decision matrix was fairly simple.


Workload

Preferred Location

Stable workloads

Owned infrastructure

Variable workloads

Public cloud


But AI inference operations introduce new variables.


Variable

Why It Matters

GPU utilization rate 

Idle GPUs are extremely expensive assets

Data gravity

Moving large datasets can be costly

Security/compliance

Some training data cannot leave enterprise control

Latency requirements

Inference may need proximity to users

Model size

Large models require specialized clusters

Elasticity

Some workloads are highly bursty

Technology obsolescence

GPUs depreciate faster than traditional servers

Capital availability

AI clusters require large up-front investments


So the new decision-making matrix requires some understanding of when public cloud, private cloud or owned facilities provide the best economics for specific workloads.


For example, public cloud remains an optimal choice when utilization is uncertain or sporadic.


AI Task

Advantage

AI experimentation

No capital investment

Proof-of-concept projects

Fast startup

Occasional model training

Rent GPUs only when needed

Seasonal demand spikes

Elastic scaling

Startup AI products

Preserve capital

New model evaluation

Access latest GPUs immediately


If GPU utilization is below roughly 30 percent to 40 percent, public cloud often is economically attractive.


But private cloud (enterprise-owned Infrastructure operated as a cloud) makes sense in other scenarios, such as trials or customer service operations, for reasons including customization, data control or security. 


AI Task

Internal enterprise copilots

Customer service AI

Financial AI applications

Healthcare AI systems

Proprietary model fine-tuning

Enterprise knowledge management AI


If workloads are predictable and GPU utilization exceeds roughly 50 percent to 60 percent, private infrastructure often becomes economically superior.


Owned facilities will make most sense for hyperscalers such as Amazon Web Services, Microsoft Azure, Google Cloud, large AI labs, major telecom operators, or very-large enterprises.


AI Task

Frontier model development

Large-scale foundation model training

Continuous AI training operations

National AI infrastructure

Massive enterprise AI platforms


As often happens with computing technology, no single solution is right for every use case. For most large enterprises, the most-likely long-term architecture for large enterprises will often use public cloud, sometimes private cloud or owned facilities in some instances. 


No solution will always be the best. 


Workload Type

Best Location

AI experiments

Public cloud

Model training bursts

Public cloud

Fine tuning proprietary models

Private cloud

Internal enterprise inference

Private cloud

Regulated data workloads

Private cloud

Consumer-facing inference spikes

Public cloud

Constant high-volume inference

Owned GPU clusters

Mission-critical AI

Hybrid


Pilots and training will normally be best suited for public cloud platforms. Proprietary models, regulated workloads or internal inference will be suited to private cloud.


Consumer-facing workload spikes are likely suited to use of public cloud, with  high-volume inference likely an option for high-volume, sustained inference operations.


Monday, June 8, 2026

Magnifica Humanitas is Not "Just" About AI

In spite of all the attention received by Magnifica Humanitas, focused on the relationship between human values and artificial intelligence, it also can be viewed within the context of the full range of social doctrine guiding the Catholic Church since the publishing of Rerum Novarum in 1891. 


Note the intentional choice of name by Pope Leo XIV (who authored Magnifica Humanitas), following Pope Leo XIII (who authored Rerum Novarum). 


The choice of name is deliberate. It implies that his pontificate will center on the Church aggressively defending human dignity and workers' rights.


Every encyclical since Rerum Novarum has elaborated a consistent body of doctrine known as the “social doctrine” of the church, compiled neatly in the document Compendium of the Social Doctrine of the Church


The thread running through every encyclical is that the human person possesses an inalienable dignity and that no economic system, political ideology, technological or ecological issue can be evaluated apart from what it does to that dignity:

  • Rerum Novarum (1891) argues that workers are not a commodity and have the right to a just wage, the right to form associations and own property.

  • Quadragesimo Anno (1931) includes the principle that decisions should be made at the lowest competent level of social organization, to support human agency and creativity.

  • Mater et Magistra (1961) discusses human rights and responsibilities

  • Pacem in Terris (1963) argues that states, like persons, are subject to a moral order they did not create

  • Populorum Progressio (1967) argues that authentic economic development is not gross domestic product  growth but the development of the whole person and every person

  • Octogesima Adveniens (1971) addresses the person in the modern city — uprooted from traditional communities, confronted with ideological pluralism, newly exposed to media and mass politics

  • Laborem Exercens (1981) argues that work matters not only because it produces goods, but because in working, the person expresses and develops themselves as a subject

  • Sollicitudo Rei Socialis discusses solidarity, a commitment to the common good

  • Centesimus Annus (1991) argues that markets are legitimate when governed by a strong juridical framework and embedded in a culture that prizes more than consumption and is shaped by concern for the common good

  • Caritas in Veritate (2009) insists that love (caritas) belongs in economic and social analysis as a principle of social architecture. He argues that the economy needs not only justice (what is owed) but gift (what is freely given beyond obligation)

  • Laudato Si' (2015) argues that the ecological crisis and the human dignity crisis are the same crisis. An economy and culture that exploits the natural world with indifference is animated by the same logic that exploits persons

  • Laudate Deum (2023) is about climate change and global governance

  • Dilexit Nos (2024) argues that  persons are not bundles of preferences or economic actors but beings with an interior life oriented toward love

  • Magnifica Humanitas (2026) extends the principle of human dignity to the use of AI. 


A persistent misreading of Catholic social encyclicals treats them as policy manifestos. 


They are instead a platform of moral principles that guide policy reasoning without determining specific policy outcomes.


In other words, the encyclicals teach ends. They do not generally prescribe the means.


The principles most commonly discussed in social teaching include:

  • Human dignity: Every person, by virtue of being human, deserves to be treated as an end, never merely as a means

  • The common good:  conditions that allow persons and communities to flourish

  • Subsidiarity: decisions should be made at the lowest level of social organization competent to make them effectively

  • Solidarity: persons and communities are genuinely responsible for one another

  • The Universal Destination of Goods: the goods of creation are destined for all persons

  • The Preferential Option for the Poor: in situations of conflict or scarcity, priority belongs to the needs of the most vulnerable.


The corollary is a high and demanding vocation for the laity. If the Church does not prescribe policies, then Catholic politicians, economists, lawyers, scientists, engineers, and citizens bear genuine responsibility for the quality of their prudential reasoning.


The Catholic tradition, outlined in Gaudium et Spes, does explicitly and formally hold that the determination of specific social, economic, and political policies falls within the proper competence of the laity, and that clergy, as clergy, have no special expertise in these matters.


Apostolicam Actuositatem (Decree on the Apostolate of the Laity) reinforces this, saying "the laity must take up the renewal of the temporal order as their own special obligation."


Christifideles Laici (1988) is perhaps the most fully developed treatment. The apostolic exhortation describes the laity's secular character as their "proper and peculiar character.” It is not accidental but essential to their vocation. 


The temporal order (politics, economics, culture, science, the professions) is the specific field of lay apostolic activity, according to Octogesima Adveniens. Clergy and religious who involve themselves in direct political activity are, in an important sense, trespassing on a domain that is not properly theirs.


But there are nuances. 


It does not mean clergy are forbidden from having opinions. Bishops and priests are citizens, often educated people, sometimes with relevant expertise in economics, law, or political philosophy quite apart from their ordination. A bishop who is also a trained economist has that economic competence as a person.


It does not mean the Church is silent on social matters. The entire tradition of social encyclicals demonstrates the opposite. What it means is that the Church's legitimate social teaching operates at the level of moral principles, not policy. 


The Catholic tradition's formal position is both clear and demanding: clergy have genuine and important authority in articulating moral principles, forming conscience, and prophetically naming clear violations of human dignity. 


The clergy has no special competence, and no legitimate authority, in determining the specific social, economic, and political policies through which those principles are imperfectly and provisionally embodied in historical institutions.


Magnifica Humanitas has to be read with all that in mind. 


Sunday, June 7, 2026

AI Infra Financing Gets Creative

Financing of AI infrastructure has evolved into a complex, multi-layered financial architecture that extends well beyond traditional corporate balance sheets. 


External financing structures include:

  • Strategic partnerships: Frontier model labs and hyperscalers are forming partnerships for regional development, power infrastructure, and equity contributions

  • Public sector and sovereign support

  • Captive markets: In some instances, state-owned enterprises or governments direct domestic demand toward local chip manufacturers.


Financing Model

Description

Example / Context

Source

Structured/Off-Balance Sheet

Using infrastructure funds and private credit to distribute risk across a layered set of claims.

General industry shift toward using private credit and structured vehicles to fund data center buildouts.

BIS

Community-First Partnerships

Joint commitments between developers and providers to share infrastructure costs and regional responsibilities.

Microsoft's "Community-First AI Infrastructure" plan and OpenAI's "Stargate Community" initiative.

HKS

National Sovereign Investment

Coordinating investments in data, compute, and algorithms through sovereign-backed frameworks.

Frameworks for "AI Triads" in low-to-middle-income countries using structured funding tranches.

Oxford

Captive Market Funding

Generating revenues through domestic mandated demand to fund internal R&D cycles.

Huawei’s AI chip revenue generation within the Chinese domestic ecosystem.

Bruegel


In many instances, the intent is to reduce capital investment requirements by moving to off balance sheet vehicles or “compute as payment” arrangements.


Hyperscaler

Model Supplier

Deal Type / Structure

Estimated Value / Capacity

Source

Google

Anthropic

Multi-year compute commitment + Equity investment

Up to $40B investment; 3.5GW TPU capacity (via Broadcom)

Silicon Republic

Amazon (AWS)

Anthropic

Compute credit + Equity investment

Up to $25B total commitment; multi-year cloud compute

Silicon Republic

Microsoft

OpenAI

Exclusive cloud provider + Multi-stage capital injection

~$10B+ in multi-year funding; 49% profit stake

Aranca

Meta

N/A (Self-build)

Structured finance (SPV) for data center buildout

~$30B "Hyperion" SPV (Blue Owl Capital led)

SoftwareSeni

Google/Anthropic

SpaceX

Compute infrastructure delivery contracts

Potentially >$70B over multi-year term

AA


As seen with Meta’s "Hyperion" transaction, hyperscalers are increasingly utilizing Special Purpose Vehicles (SPVs) and partnerships with private credit firms (e.g., Blue Owl Capital) to fund massive data center buildouts. This allows the companies to offload the capital intensity of the physical build while retaining operational control and capacity priority.


In many of these deals, "compute" has become a literal form of payment. The Google-Anthropic and Amazon-Anthropic deals are not merely cash-for-equity; they are deeply intertwined with multi-gigawatt (GW) capacity commitments and customized hardware access (such as Google’s TPUs).


Financing is no longer focused just on chips. The capital is increasingly directed toward the "AI Triad"—the integration of compute, dedicated energy infrastructure, and data center physical shells. This is evidenced by the trend of co-locating data centers with renewable energy sources and the invocation of national defense acts (as seen in the U.S. in early 2026) to prioritize grid expansion for AI.


Orbital AI Compute Seems to be Coming, but Not at Scale, Right Away

With SpaceX going public on June 12, 2026, lots of investors will be pondering the feasibility of creating orbital data centers at scale. B...